Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:16000
loss:CoSENTLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use Pascalymb/result_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Pascalymb/result_model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Pascalymb/result_model") sentences = [ "A man and woman are walking in a restaurant that has signs in Chinese.", "A newlywed couple is walking through a Chinese restaurant.", "The woman is sitting on the ground.", "they are playing basketball" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a5fb4c7cd8d9eff3f75c8681d8a790024667a1ea8201f4adccda73f40dbd1823
- Size of remote file:
- 5.52 kB
- SHA256:
- c1e40ce4a645b063d5f48aaca9cf8033476f65eb7a389ce8b51de1b5de8073f2
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